Senior Sustainability Scientist

About the job

Translate LCA (life cycle assessment) and carbon accounting theory into operational, repeatable methodologies that can be executed at scale across cloud hardware portfolios. Lead large, cross-functional v-teams and independently drive ambiguous sustainability problems to completion; set direction, execute, and deliver measurable outcomes. Serve as a trusted applied science expert for sustainability decision-making across carbon and waste workstreams. Design, define, and operationalize sustainability methodologies (e.g., LCA governance and validation) and drive approval through established governance bodies and stakeholders. Act as a data steward, validating sustainability data and impact statements before communicating them to internal and external stakeholders; drive continuous improvement of data quality and completeness. Produce clear documentation of work, experiments, methodology decisions, and validation outcomes to ensure rigor, repeatability, and audit readiness. Develop and scale automation for repeatable sustainability processes (e.g., intake/triage of methodology requests, validation workflows, quality checks), freeing capacity for strategy and execution. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ years related experience (e.g., statistics predictive analytics, research) OR Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) OR equivalent experience. PhD in Civil & Environmental Engineering (or related discipline) focused on LCA and asset-level sustainability modeling. Experience in organizational carbon accounting and scalable LCA-based footprinting, including development of automated tools. Proven ability to lead complex, cross-org sustainability data programs and deliver independently at a senior level. Demonstrated experience applying scientific methods (experimentation, modeling, validation) to real-world operational problems with measurable impact. Experience building automation or AI-assisted workflows for data validation or operational decision support. Familiarity with designing governance processes with validation criteria, data quality KPIs, and stakeholder review forums.